Abstract
Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) provides high throughput in communication. In SSVEP-BCI, typically, higher accuracy can be achieved with a relatively longer response time. It is therefore a research topic to reduce the response time while keeping high accuracy. We propose a new method, temporal alignments enhanced Canonical Correlation Analysis (TACCA), followed by a decision fusion to improve classification accuracy with short response time. TACCA exploits linear correlation with non-linear similarity between steady-state responses and stimulus frequencies. We compare TACCA and three state-of-the-art methods using data from 54-subjects with response time ranging from 0.5 to 4 seconds. The evaluation results show that TACCA yields mean significant accuracy increase of 10-30% in all segment lengths, especially for the shorter time segment. One-way ANOVA tests show high significant differences between single and multiple phases in TACCA performance.
| Original language | English |
|---|---|
| Title of host publication | 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 |
| Publisher | IEEE Computer Society |
| Pages | 155-158 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538679210 |
| DOIs | |
| Publication status | Published - 2019 May 16 |
| Externally published | Yes |
| Event | 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States Duration: 2019 Mar 20 → 2019 Mar 23 |
Publication series
| Name | International IEEE/EMBS Conference on Neural Engineering, NER |
|---|---|
| Volume | 2019-March |
| ISSN (Print) | 1948-3546 |
| ISSN (Electronic) | 1948-3554 |
Conference
| Conference | 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 |
|---|---|
| Country | United States |
| City | San Francisco |
| Period | 19/3/20 → 19/3/23 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Mechanical Engineering
Cite this
Improving the Performance of SSVEP BCI with Short Response Time by Temporal Alignments Enhanced CCA. / Phyo Wai, Aung Aung; Lee, Min Ho; Lee, Seong Whan; Guan, Cuntai.
9th International IEEE EMBS Conference on Neural Engineering, NER 2019. IEEE Computer Society, 2019. p. 155-158 8716985 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2019-March).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improving the Performance of SSVEP BCI with Short Response Time by Temporal Alignments Enhanced CCA
AU - Phyo Wai, Aung Aung
AU - Lee, Min Ho
AU - Lee, Seong Whan
AU - Guan, Cuntai
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) provides high throughput in communication. In SSVEP-BCI, typically, higher accuracy can be achieved with a relatively longer response time. It is therefore a research topic to reduce the response time while keeping high accuracy. We propose a new method, temporal alignments enhanced Canonical Correlation Analysis (TACCA), followed by a decision fusion to improve classification accuracy with short response time. TACCA exploits linear correlation with non-linear similarity between steady-state responses and stimulus frequencies. We compare TACCA and three state-of-the-art methods using data from 54-subjects with response time ranging from 0.5 to 4 seconds. The evaluation results show that TACCA yields mean significant accuracy increase of 10-30% in all segment lengths, especially for the shorter time segment. One-way ANOVA tests show high significant differences between single and multiple phases in TACCA performance.
AB - Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) provides high throughput in communication. In SSVEP-BCI, typically, higher accuracy can be achieved with a relatively longer response time. It is therefore a research topic to reduce the response time while keeping high accuracy. We propose a new method, temporal alignments enhanced Canonical Correlation Analysis (TACCA), followed by a decision fusion to improve classification accuracy with short response time. TACCA exploits linear correlation with non-linear similarity between steady-state responses and stimulus frequencies. We compare TACCA and three state-of-the-art methods using data from 54-subjects with response time ranging from 0.5 to 4 seconds. The evaluation results show that TACCA yields mean significant accuracy increase of 10-30% in all segment lengths, especially for the shorter time segment. One-way ANOVA tests show high significant differences between single and multiple phases in TACCA performance.
UR - http://www.scopus.com/inward/record.url?scp=85066733929&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066733929&partnerID=8YFLogxK
U2 - 10.1109/NER.2019.8716985
DO - 10.1109/NER.2019.8716985
M3 - Conference contribution
AN - SCOPUS:85066733929
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 155
EP - 158
BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PB - IEEE Computer Society
ER -